hey, i'm
Samuel
Jayasingh
ai/ml engineer building intelligent systems.
currently @ spritle software · chennai, india.
$ cat ./about.md
i'm a junior software engineer specialising in artificial intelligence at spritle software, where i research and develop generative AI models — transformers, diffusion models, gans, and vaes.
my work spans llm-powered applications, multi-agent orchestration systems, rag pipelines, and production-grade ml — from industrial failure prediction to ai chatbots and image generation platforms.
b.tech in artificial intelligence and data science · rajalakshmi institute of technology · cgpa 8.0/10.
technologies
$ ls -la ./experiences
Jan 2026 – Present
Junior Software Engineer, AI
Spritle Software · Chennai
- researching and developing generative ai models: transformers, diffusion, gans, vaes
- building llm-powered applications and ml pipelines with pytorch, tensorflow, hugging face
- optimising model performance and developing production-grade ai systems
Oct 2025 – Jan 2026
Software Engineer Intern, AI
Spritle Software · Chennai
- designed agentic ai architectures evolving from single-agent to multi-agent orchestration
- implemented rag pipelines using fastapi, chromadb, and async embedding workflows
- built api toolkits and postgresql streaming query tools using claude sdk and mcp servers
- developed automated vulnerability scanning and remediation workflows
Jul – Dec 2024
AI Automation Intern
Vleafy Technologies · Chennai
- built conversational ai chatbots using llms and nlp for customer interaction
- automated backend workflows with python and gupshup apis
- integrated ai modules into full-stack web apps via restful services
Jul 2023 – Jan 2024
Data Analyst & ML Intern
ZF Group – WABCO India · Chennai
- designed a dynamic bus tracking system using mapping apis
- processed and engineered features from large-scale route data
- developed automated python dashboards for monitoring system metrics
$ cat projects.json
mcp server exposing 40+ gitlab operations as structured tools for ai clients like claude and cursor — repo management, branch protection, merge requests, issue tracking, ci/cd log retrieval, and cross-project search. includes a standalone gemini-powered autonomous agent.
python · mcp · gitlab api · pydantic · docker
fine-tuned qwen3.5-0.8b using lora via the unsloth framework on adversarial datasets for red-teaming and safety evaluation research. reduced training loss from ~1.88 to ~1.39 over 60 steps. built for robustness benchmarking, not deployment.
python · lora / qlora · unsloth · hugging face · pytorch
powershell tool that removes 40+ pre-installed bloatware apps, disables telemetry and cortana, strips bing from windows search, and declutters the taskbar and file explorer on windows 10/11. supports preset and granular modes with full rollback via registry files.
powershell · batch · windows 10/11 · registry
end-to-end ml pipeline for predicting industrial equipment failures from sensor telemetry. uses autoencoder-based anomaly detection, feature engineering on time-series data, apache airflow for orchestration, and mlflow for experiment tracking and model versioning.
python · autoencoders · apache airflow · mlflow · pytorch
multi-agent knowledge answering system with specialised agents for documents, apis, databases, and web sources. implements task routing, short-term memory, and tool-based reasoning across heterogeneous data sources.
python · langchain · rag · multi-agent · chromadb
agentic cli tool for automated application and infrastructure security analysis. autonomous agents handle threat modelling, cve mapping, and remediation suggestion across codebases and deployment configs.
python · llms · security · cli
$ ls -la ./blog
a deep dive into designing production multi-agent architectures — task routing, memory management, tool orchestration, and the pitfalls to avoid.
what changes when you move a rag system from a notebook to prod — chunking strategies, embedding choices, reranking, and async retrieval at scale.
a practical guide to fine-tuning open-source llms using peft and qlora — from dataset curation and formatting to training and quantised inference.
how i built mcp servers to expose gitlab apis and postgresql as structured tools for ai clients — architecture decisions and lessons learned.
implementing ddpm in pytorch from first principles — the math behind forward/reverse diffusion, noise schedules, and training a small image model.
$ ./contact.sh
let's build
something.
always open to interesting problems, collaborations, and new opportunities. whether it's ai, full-stack, or something in between — reach out.
samueljayasingh77@gmail.com ↗